معرفی
Tomaž Hočevar serves as an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he is an active member of the Bioinformatics Laboratory. He teaches core undergraduate courses including Algorithms and Data Structures 1, Programming 2, and Machine Learning for Data Science 1, while significantly contributing to competitive programming through national contest organization and mentorship for both university and high-school students.
His research integrates Algorithms and Data Structures, Machine Learning, and Network Analysis with a focus on local structural properties in biological networks. Current applications target circular RNA in liver cancer (project J3-4513), explainable foundation models for human gene expression (project L2-60154), and water-linked epidemiology of plant viruses (project L4-3179). He extends this work to educational domains through the DALI4US project developing data literacy curricula for primary schools.
While only one 2014 publication on graphlet counting is explicitly cited, his active research portfolio reveals a strong trajectory in computational biology. Current projects emphasize explainable AI for genomic data interpretation, nanopore sequencing applications, and interdisciplinary approaches bridging computer science with oncology and plant virology, indicating significant methodological innovation in biological data analysis.
No scientific awards were mentioned in the provided text.
Dr. Hočevar secures competitive research funding primarily through the Slovenian Research Agency (ARRS) and Structural Funds:
- Current projects: J3-4513 (circular RNA in liver cancer, 2022-2025), DALI4US (data literacy for primary schools, 2024-2026), L2-60154 (explainable foundation models for gene expression, 2025-2027)
- Past projects: ARRS AI research program (2015-2020), BioPharm.si biologics initiative (2016-2020), L7-2632 nanopore sequencing (2020-2023), L4-3179 tobamovirus epidemiology (2021-2024)
He maintains active collaboration within the Bioinformatics Laboratory at FRI, focusing on genomic data analysis pipelines, machine learning tool development for biological applications, and interdisciplinary research teams addressing cancer genomics and agricultural virology challenges through computational approaches.
